#1 Lecture .pptx

Artificial Intelligence
Lecture 1
Introduction

What is Artificial Intelligence ?
• Human are wise because of their intelligence.
• Human have tried to understand how they:
• Think
• Perceive
• Understand
• Predict
• Manipulate
Large and complicated world
The field of AI is an attempts not just to understand
human intelligence but also build intelligent entities

• AI is one of the relatively new fields in science.
• Work on AI started after the Word War II
• The term AI was coined in 1956
• AI cover a huge variety of subfields:
• Play chess
• Proving mathematical theorems
• Writing poetry
• Driving a car
• Diagnosis disease
AI is truly a universal field

• The definitions on top are concerned with
thought process and reasoning.
• The one in the bottom address behavior.
• The definitions of the left measure success in
terms of fidelity (similarity) to human
performance.
• The one in the right measure success against an
ideal performance (rationality)

• AI definitions are laid out along
different dimension
• 1. process and reasoning (thinking)

• AI definitions are laid out along
different dimension
• 2. Behavior

• AI definitions are laid out
along different dimension
• 3. fidelity to human
performance

• AI definitions are laid out
along different dimension
• 4. Ideal performance
(Rationality)
A system is rational if it does the
“right thing” given what it knows

1. Acting Humanly: the Turing Test
• Proposed by Alan Turing (1950)
A computer passes the test if a human
interrogator, after posing some written
questions, cannot tell whether the
written response come from a person or
from a computer.
Ex : Which one is bigger a small
mountain or a big cat

To pass the Turing test the computer need to posses the following
capabilities:
1. Natural language processing
2. Knowledge representation to store what it knows or hears
3. Automated reasoning
4. Machine learning to adapt to new circumstances

• Turing Test avoided direct physical interaction between the
interrogator and the computer.
• However, the so called Total Turing Test includes:
• Video Signal for perceptual abilities.
• Ability to pass physical objects.
Hence Total Turing Test will need
5. Computer vision to perceive objects
6. Robotics to manipulate objects and move around.

2. Thinking humanly: The cognitive
modeling approach
• Program think like a human
• How human thinks
• Actual working of human mind
Represent human mind as a
computer program

2. Thinking humanly: The cognitive
modeling approach
• Program behavior matches
corresponding human behavior.
• Example:
General Problem Solver “GPS”
Compared and trace its reasoning
steps to traces of human subject

3. Thinking rationally: The “laws of
though” approach
• Right Thinking
• Irrefutable (indisputable) reasoning
process
Socrates is a man, all men are mortal,
therefore, Socrates is mortal

3. Thinking rationally: The “laws of
though” approach
• Represent object in the word and
the relation between them in form
of logical annotation
• Obstacle of this approach:
1. Knowledge representation with
uncertainty.
2. Computational resources

4. Acting rationally: The rational agent
approach
• Agent is something that acts
• Computer agent are expected:
• Operate autonomously
• Perceive their environment
• Persist over time
• Adapt to change
• Create and pursue goals
Rational Agent is one that acts to achieve
best outcome
In AI rational mean make the correct
inference

The foundation of Artificial Intelligence
1. Philosophy:
• How can form a rules to be used to draw a valid conclusions?
• How does the mind arise from a physical brain?
• Where dose knowledge come from?
• How does knowledge lead to action?
2. Mathematics
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?

The foundation of Artificial Intelligence
3. Economics
• How should we make decisions so as to maximize payoff?
• How should we do this when others may not
• How should we do this when the payoff may be far in the future?
4. Neuroscience
• How do brains process information?
5. Psychology
• How do humans and animals think and act?
6. Computer engineering
• How can we build an efficient computer
7. Control theory and cybernetics
• How can artifacts operate under their own control
8. Linguistics
• How does language related to thought

The History of Artificial Intelligence
The gestation of AI (1943-1955) Warren (1943) : constructed artificial neurons characterized as being “on” or
“off” in response to neighboring neurons. neurons are connected using
logical connectives (and, or , not , etc )
Donald (1949) updated the previous work by adding connection strengths.
Marvin (1950) build the first neural network computer (SNARC) to simulate
a B-24 automatic pilot which consists of 40 neurons
The birth of AI (1956) Tow months work shop held on Dartmouth college in Hanover in the summer
of 1956
Early enthusiasm (1952-1969) General Problem Solver (GPS): Thinking humanly, Physical symbol system
hypothesis which has sufficient means for general intelligent action, IBM
geometry theorem prover which was able to prove theorems that many
students of mathematics would find quite difficult.
A dose of reality (1966-1973) Early successes made a prediction that within 10 years would do many
intelligence actions. The prediction come true within 40 years due to two
main difficulty: absence of background knowledge and the need for faster
hardware and memory

Knowledge-based system
(1969-1979)
Early stages of AI trying to string together elementary reasoning steps to
find complete solutions (Weak Methods).
Alternatively , using more powerful (domain specific knowledge ) allows
larger reasoning steps (expert system)
AI becomes an industry (1980-
present)
The first successful commercial AI (expert system) R1 began operation the
digital equipment corporation.
By 1988, the corporation used 40 expert systems
The return of neural networks
(1986-present)
In the mid of 1980s for different research group reinvented the back-
propagation learning algorithm firs found in 1969.
AI adopts the scientific method
(1987-present)
Revolution in both the content and methodology of work in AI. It is more
common to build on existing theories than to propose brand-new one.

The emergence of intelligent
Agent (1995-present)
Internet is one of the most important factor to create agent. It is so common
in web-base application that the “-bot” , recommender system , search
engines.
The availability of very large
data sets (2001-present)
In AI recent work has been suggests that it make more sense to concentrate
on the data. This is true because of the increasing availability of very large
data sources.

The State of the Art
What can AI do today ?
1. Robotic vehicles
2. Speech recognition
3. Autonomous planning and scheduling
4. Game playing
5. Spam fighting
6. Logistics planning
7. Robotics
8. Machine Translation

Summary
Important Points
1. Different people approach AI with different
goals in mind (Thinking Or Behavior).
2. Intelligence is concerned mainly with rational
action (intelligent agent).
3. Philosophers (400 B.C.) consider mind like
machine, mind operates on knowledge encoded
in some internal language, and that thought can
be used to choose what actions to take.
4. Mathematicians provided the tools to
manipulate statements of logical certainty as
well as uncertain (probabilistic) and reasoning

Summary
Important Points
5. Economists formalized the problem of making
decisions.
6. Neuroscientist discovered some facts about how
brain works
7. Psychologists adopted the idea that human can
be considered as information processing
machine
8. Computer engineers provided the ever-more-
powerful machine that make AI applications
possible

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